RMark: Plotting the effects of time-varying covariates

Hello list.
I am running a multi-state analysis, in which I have multiple 'design' (time-varying, population-level) enviromental covariates in my model for S (apparent survival). I am trying to work out the best way to present my results, but struggling a bit.
I have found quite a bit of info online about plotting the effect of individual covariates, but not much on how to do the same for time-varying/design covariates. I would like to show plots of predicted survival against a range of values of my environmental covariates. But, I can't work out a way to do this when I have multiple covariates in my models.
For example, if my model for S has the covariates winter temperature and winter rainfall: I have two covariates affecting survival at each time step, so I can't just plot the real estimate of survival against the value of one covariate (eg. rain) at the same time step, as the S value will also be representative of the effects of the other covariate (temperature) at that same time step (so won't be representative of only the rain effect). Besides I think I have read that the real parameter estimates in the model output represent estimates at the mean values for the covariates.....
So, how can I create plots of predicted S where one covariate (e.g. temp) is held at the mean and the other (e.g. rain) is varied within the range of sampled values? This is a pretty straight forward thing to do in regression type models, but I can't seem to work it out for RMark!
I have read that you can use the function covariate.predictions (documented for use with individual covariates) to produce these types of estimates; but I don't understand how (and haven't been able to find a worked example of this). Would I need to go back to the encounter history data frame and add in the values for each of my covariates alongside each individual's ch data in columns for each year (with the same value for each year among individuals)? This seems like an extremely long-winded work around (especiallywhen I am using multiple covariates and 30 years worth of data on >5000 individuals)!
Hopefully someone can help me out with this.
Thanks very much,
Alice
I am running a multi-state analysis, in which I have multiple 'design' (time-varying, population-level) enviromental covariates in my model for S (apparent survival). I am trying to work out the best way to present my results, but struggling a bit.
I have found quite a bit of info online about plotting the effect of individual covariates, but not much on how to do the same for time-varying/design covariates. I would like to show plots of predicted survival against a range of values of my environmental covariates. But, I can't work out a way to do this when I have multiple covariates in my models.
For example, if my model for S has the covariates winter temperature and winter rainfall: I have two covariates affecting survival at each time step, so I can't just plot the real estimate of survival against the value of one covariate (eg. rain) at the same time step, as the S value will also be representative of the effects of the other covariate (temperature) at that same time step (so won't be representative of only the rain effect). Besides I think I have read that the real parameter estimates in the model output represent estimates at the mean values for the covariates.....
So, how can I create plots of predicted S where one covariate (e.g. temp) is held at the mean and the other (e.g. rain) is varied within the range of sampled values? This is a pretty straight forward thing to do in regression type models, but I can't seem to work it out for RMark!
I have read that you can use the function covariate.predictions (documented for use with individual covariates) to produce these types of estimates; but I don't understand how (and haven't been able to find a worked example of this). Would I need to go back to the encounter history data frame and add in the values for each of my covariates alongside each individual's ch data in columns for each year (with the same value for each year among individuals)? This seems like an extremely long-winded work around (especiallywhen I am using multiple covariates and 30 years worth of data on >5000 individuals)!
Hopefully someone can help me out with this.
Thanks very much,
Alice